The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
Accelerated Future Learning via Explicit Instruction of a Problem Solving Strategy
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
The dynamics between student affect and behavior occurring outside of educational software
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
User Modeling and User-Adapted Interaction
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This study compared three pedagogical approaches on the acquisition and robust understanding of the control of variables strategy (CVS). In Sao Pedro et al. (2009), we showed that two direct learning conditions (with and without reification) significantly outperformed the discovery learning condition for constructing unconfounded experiments starting from an initially multiply confounded experimental setup. In the study described here, we retested 57 students six months later on constructing unconfounded experiments in a virtual ramp environment, solving problems requiring CVS, and explaining CVS. Collapsing over time, we found that the direct+reify condition had more robust learning than either the direct-no reify or discovery learning conditions on constructing unconfounded experiments. At the delayed posttest, we found a strong trend favoring the direct+reify condition over the other conditions as measured by tasks designing unconfounded experiments starting from a multiply confounded state.